In the experiments, LEEP and the linear classifier are the second best ranking methods for the checkpoints pre-trained for classification. However, LEEP's computation cost is high when a checkpoint's classification output is high-dimensional (e.g., iNaturalist contains more than 8000 classes). Besides, its softmax estimation of the classification probability \(\theta (x)_z\)  is often poorly calibrated [1]}. Finally, it does not apply to the checkpoints with no classification heads.
